maddaisy – cultivating innovation

Tag: mckinsey

  • The $5 Trillion Business Transfer That Has Nothing to Do with AI

    Maddaisy has spent the past fortnight examining McKinsey through the lens of AI — its 25,000 AI agents, its shift to outcome-based pricing, its OpenAI Frontier Alliance. But the firm’s most consequential publication this month may have nothing to do with artificial intelligence at all.

    A new report from McKinsey’s Institute for Economic Mobility identifies what it calls the “Great Ownership Transfer” — roughly six million small and medium-sized American businesses facing ownership transitions by 2035, representing up to $5 trillion in enterprise value. The cause is straightforward demography: baby boomer business owners are retiring, and the systems for handing their companies to the next generation of owners barely exist.

    A 92% Closure Rate

    The headline statistic is stark. Today, 92% of small-business market exits in the United States result in closure. Just 5% are completed as sales. Another 3% are transferred to new owners through other mechanisms. The remaining businesses simply shut their doors.

    This is not a failure of entrepreneurial ambition. Small businesses account for 99% of all US companies and employ nearly half the national workforce. The problem, as the report’s authors put it, is structural: “Buying and selling a small business is often harder than starting one because the systems that support entrepreneurship in the United States are currently built for founding companies, not transferring them.”

    Viable firms are closing not because they lack customers or revenue, but because the pathways to succession are limited, opaque, and prohibitively expensive for the buyers who would keep them running.

    The Missing Middle

    The risk concentrates in what McKinsey terms the “missing middle.” Nearly 80% of projected exits involve micro and emerging middle-market businesses valued at less than $2 million. These firms sit in an awkward no-man’s-land: too small to attract private equity or institutional acquirers, but too large and complex for a typical first-time buyer to finance without significant support.

    The consequences are not evenly distributed. Rural communities, where these smaller firms often serve as anchor employers and the primary local tax base, face disproportionate exposure. Labour-intensive industries essential to daily life — retail, construction, food services — account for roughly one-third of the businesses caught in this gap. When a town’s only plumbing contractor or hardware shop closes because there is no viable buyer, the impact extends well beyond the balance sheet.

    A Financing System Built for the Wrong Problem

    The report is pointed about where the infrastructure fails. The SBA 7(a) loan — the primary federal instrument for small-business acquisition financing — requires high equity contributions and full personal guarantees. For first-time buyers, particularly those without existing assets or family wealth to pledge, the barrier is effectively insurmountable.

    The broader ecosystem compounds the problem. Unlike residential property, where standardised appraisals, mortgage products, and regulatory frameworks have created a liquid market, small-business transfers remain bespoke transactions. Each deal requires its own valuation, its own legal structure, its own financing arrangement. There is no MLS for Main Street businesses, no standardised underwriting for a family-owned electrical contractor with $1.5 million in revenue.

    McKinsey’s prescription is to treat small-business acquisition as a scalable market rather than a series of one-off transactions. That means modernised underwriting standards, bundled advisory services, and coordinated market infrastructure — the kind of systemic plumbing that already exists for property and public equities but has never been built for SMB ownership transfer.

    The Equity Dimension

    The demographic profile of current small-business owners — overwhelmingly older, white, and male — means this transfer also carries significant implications for wealth distribution. Under current patterns, women, Black, and Latino individuals combined would capture only about 28% of the transferring $5 trillion in value.

    McKinsey’s modelling suggests the gap is not inevitable. If ownership participation reached demographic parity, Black individuals could see their wealth capture increase more than fourfold to approximately $369 billion. Parity for women could unlock roughly $700 billion. These are not aspirational targets plucked from a diversity statement — they are the arithmetic consequence of removing structural barriers to acquisition financing and deal flow.

    The growth of employee stock ownership plans (ESOPs) and cooperative conversions offers one partial pathway. ESOP participation has grown by more than one million participants over the past decade, creating models for equitable ownership transfer that do not depend solely on traditional private capital. New platforms — BizBuySell, Acquire.com, Baton — are beginning to chip away at the opacity of the SMB transaction market, though none yet operates at the scale the problem demands.

    What This Means for Consulting

    For the consulting industry, the report represents both a diagnosis and an opportunity. Advisory firms have spent the past two years racing to build AI practices and secure platform alliances. The $5 trillion ownership transfer is a reminder that some of the largest structural challenges in the economy are not technology problems at all — they are market design problems, financing problems, and coordination failures.

    Succession advisory, M&A support for sub-$2 million transactions, ESOP conversion guidance, and community economic development are not glamorous practice areas. They do not generate the headlines that AI partnerships attract. But if McKinsey’s numbers are even roughly correct, the demand for this kind of advisory work is about to grow substantially — and the firms that build credible practices now will find themselves serving a market that barely existed a decade ago.

    The question, as the report frames it, is whether the coming decade becomes “a story of tragic business loss” or “the inflection point when business ownership became a broader pathway to mobility.” The answer will depend less on any single technology and more on whether the market infrastructure catches up to the demographic reality already underway.

  • OpenAI’s Frontier Alliance Confirms What Consultants Already Knew: AI Vendors Cannot Scale Alone

    OpenAI announced on 23 February that it has formed multi-year “Frontier Alliances” with McKinsey, Boston Consulting Group, Accenture, and Capgemini. The four firms will help sell, implement, and scale OpenAI’s Frontier platform — an enterprise system for building, deploying, and governing AI agents across an organisation’s technology stack.

    For readers who have been following maddaisy’s coverage of the consulting industry’s AI pivot, this is not a surprise. It is the logical next step in a pattern that has been building for months — and it tells us more about the limits of AI vendors than about the ambitions of consulting firms.

    The vendor cannot scale alone

    The most revealing line in the announcement came from Capgemini’s chief strategy officer, Fernando Alvarez: “If it was a walk in the park, OpenAI would have done it by themselves, so it’s recognition that it takes a village.”

    That candour is worth pausing on. OpenAI’s enterprise business accounts for roughly 40% of revenue, with expectations of reaching 50% by the end of the year. The company has already signed enterprise deals with Snowflake and ServiceNow this year and appointed Barret Zoph to lead enterprise sales. Yet it still needs consulting firms — with their existing client relationships, implementation expertise, and organisational change capabilities — to get its technology into production at scale.

    This is not a story about OpenAI’s generosity in sharing the enterprise market. It is an admission that the gap between a capable AI platform and a working enterprise deployment remains stubbornly wide. As maddaisy reported last week, PwC’s 2026 CEO Survey found that 56% of chief executives still cannot point to measurable revenue gains from their AI investments. The technology is not the bottleneck. Integration, governance, and organisational readiness are.

    A clear division of labour

    The alliance structure reveals how OpenAI sees the enterprise AI value chain. McKinsey and BCG are positioned as strategy and operating model partners — helping leadership teams determine where agents should be deployed and how workflows need to be redesigned. BCG CEO Christoph Schweizer noted that AI must be “linked to strategy, built into redesigned processes, and adopted at scale with aligned incentives.”

    Accenture and Capgemini take the systems integration role: data architecture, cloud infrastructure, security, and the unglamorous work of connecting Frontier to the CRM platforms, HR systems, and internal tools that enterprises actually run on. Each firm is building dedicated practice groups and certifying teams on OpenAI technology. OpenAI’s own forward-deployed engineers will sit alongside them in client engagements.

    This two-tier model — strategy at the top, integration at the bottom — maps neatly onto the consulting industry’s existing hierarchy. It also creates a clear dependency: OpenAI provides the platform, the consultancies provide the last mile.

    The maddaisy continuity thread

    This announcement intersects with several stories maddaisy has been tracking. When we examined McKinsey’s 25,000 AI agent deployment, the question was whether the firm’s aggressive internal build-out was a first-mover advantage or an expensive experiment. The Frontier Alliance suggests McKinsey is now positioning that internal capability as a credential — evidence that it can deploy agentic AI at scale, which it can now offer to clients through the OpenAI partnership.

    Similarly, when maddaisy covered the shift from billable hours to outcome-based consulting, the question was how firms would make the economics work. Vendor alliances like this provide part of the answer: the consulting firm brings the implementation expertise, the AI vendor provides the platform, and the client pays for outcomes rather than hours. The risk is shared across the chain.

    And Capgemini’s dual bet — adding 82,300 offshore workers while simultaneously investing in AI — now makes more strategic sense. The offshore delivery capacity is precisely what is needed to operationalise Frontier at enterprise scale. The bodies and the bots are not competing; they are complementary.

    The SaaS vendors should be nervous

    As Fortune noted, the Frontier Alliance creates a specific tension for established software-as-a-service vendors. Salesforce, Microsoft, Workday, and ServiceNow all depend on these same consulting firms to market and deploy their products. Now those consultants will also be actively promoting an alternative platform — one that positions itself as a “semantic layer” sitting above the traditional SaaS stack.

    The consulting firms are not choosing sides. They are hedging. Accenture, for instance, signed a multi-year partnership with Anthropic in December 2025 and is now a Frontier Alliance member. The firms will sell whichever platform best fits a given client’s needs, which gives them leverage over the AI vendors rather than the other way around.

    For the SaaS incumbents, however, having McKinsey and BCG actively evangelise an AI-native alternative to C-suite buyers is a development they will not welcome. Investor anxiety in this space is already elevated — shares of several enterprise software companies have been punished over concerns that customers will choose AI-native platforms over traditional offerings.

    What to watch

    The Frontier Alliance is a partnership announcement, not a set of outcomes. The real test is whether this model — AI vendor plus consulting firm — can close the deployment gap that has kept enterprise AI adoption stubbornly below expectations.

    Three things matter from here. First, whether the certified practice groups produce measurably better outcomes than the piecemeal implementations enterprises have been attempting on their own. Second, whether Frontier’s “semantic layer” architecture genuinely simplifies agent deployment or simply adds another platform layer to an already complex stack. And third, whether the consulting firms’ simultaneous alliances with competing AI vendors — OpenAI, Anthropic, Google — create genuine client value or just a more complicated sales cycle.

    For practitioners, the immediate signal is clear: the enterprise AI market is consolidating around a vendor-plus-integrator model. If your organisation is planning an agentic AI deployment, the question is no longer which model to use. It is which combination of platform, integrator, and operating model redesign will actually get agents into production — and keep them there.

  • From Billable Hours to Shared Risk: Consulting’s AI-Driven Business Model Shift

    McKinsey’s 25,000 AI agents grabbed the headlines, but the more consequential number is the roughly one-third of the firm’s revenue now tied to outcome-based engagements. Across the industry, AI is not just changing how consultants work – it is rewriting how they get paid.

    When maddaisy.com examined McKinsey’s 25,000 AI agent deployment last week, the focus was on scale: was deploying one digital agent for every 1.6 human employees a first-mover advantage or an expensive experiment? The answer may depend less on the agent count and more on a quieter transformation happening in parallel – the shift from selling time to selling outcomes.

    The Model That Built an Industry Is Under Pressure

    For decades, consulting has run on a straightforward exchange: expertise for time, billed by the hour or the project. It is a model that has produced extraordinary margins, but it carries an inherent misalignment. Consultants profit from the complexity of a problem, not necessarily from solving it quickly.

    AI agents threaten that dynamic directly. When an algorithm can synthesise research in minutes that previously took analysts weeks, the hours-based model starts to look exposed. McKinsey’s own data – 1.5 million hours saved on search and synthesis work – quantifies exactly how much billable time AI has already removed from the equation.

    Rather than watching margins erode, McKinsey is pivoting. Speaking on the HBR IdeaCast in February, CEO Bob Sternfels confirmed that outcome-based engagements – where McKinsey co-invests alongside clients and ties fees to measurable business results – now account for roughly a third of the firm’s revenue. Two years ago, that figure was negligible.

    The Economics Only Work with AI

    This is where the 25,000 agents become strategically coherent. Outcome-based consulting is inherently riskier than fee-for-service; the firm only earns if the client succeeds. To make the economics work, you need two things: lower delivery costs and higher confidence in results.

    AI agents address both. QuantumBlack, McKinsey’s 1,700-person AI division, now drives 40% of the firm’s total work. Non-client-facing headcount has fallen 25%, while output from those teams has risen 10% – the “25 squared” model. The savings create the margin headroom needed to absorb the risk of outcome-based pricing.

    It is not just McKinsey making this calculation. BCG has deployed “forward-deployed consultants” who build AI tools directly within client organisations, effectively embedding methodology as software rather than slides. Capgemini has trained 310,000 employees on generative AI, though its agentic AI bookings only reached 10% of quarterly pipeline by Q4 2025. Accenture has stopped reporting AI bookings separately because, as the firm noted in its Q1 fiscal 2026 results, AI is now embedded in virtually every engagement.

    The Client Side of the Equation

    The timing is not coincidental. As maddaisy.com reported last week, PwC’s 2026 CEO Survey found that 56% of chief executives still cannot demonstrate revenue gains from AI. When the client cannot prove value, the consultancy offering to underwrite outcomes holds a powerful negotiating position – essentially saying, “we believe in this enough to stake our fees on it.”

    The irony is that consultancies are asking clients to trust their AI capabilities while the industry’s own track record on AI delivery remains uneven. Deloitte’s own 2026 State of AI report found that 42% of organisations consider their AI strategy “highly prepared” but feel markedly less ready on infrastructure, data governance, and talent.

    McKinsey faces this credibility gap from a different direction. The firm’s State of AI research found that only 5% of companies globally see AI hitting their bottom line. Positioning itself as the firm that can deliver measurable outcomes means McKinsey is, in effect, claiming to solve a problem that its own research says almost no one has solved.

    What Changes for Practitioners

    For consultants and the organisations that hire them, three practical implications stand out.

    Procurement shifts. If outcome-based pricing becomes the norm, procurement teams will need to evaluate consulting engagements more like joint ventures than service contracts. That means assessing the firm’s AI capabilities, data infrastructure, and delivery methodology – not just the partner’s credentials and the day rate.

    The talent model is splitting. Sternfels has been explicit that McKinsey wants “great consultants and/or great technologists, groomed to be both.” The traditional path – from analyst to associate to engagement manager – now runs alongside a technical track where consultants build and deploy AI systems. BCG’s vibe-coding consultants are an early version of this hybrid role.

    Governance becomes shared. When a consultancy co-invests in outcomes and deploys AI agents within a client’s operations, the governance question becomes bilateral. As maddaisy.com has covered extensively, the gap between AI deployment speed and governance maturity is already the defining risk of 2026. Outcome-based models widen this gap further, because neither party has clear precedent for who owns the risk when an AI agent produces flawed analysis that drives a business decision.

    The Bigger Picture

    The consulting industry has weathered previous disruptions – offshoring, automation, the rise of in-house strategy teams – by evolving its value proposition. This time, the change is structural. AI agents do not just reduce the cost of delivering advice; they make it possible to charge for results instead.

    McKinsey’s 25,000 agents are best understood not as a technology deployment but as a financial instrument – the infrastructure that underwrites a new revenue model. Whether the model works depends on something no agent can automate: whether clients actually achieve the outcomes both parties are betting on.

  • McKinsey’s 25,000 AI Agents: First-Mover Advantage or the Industry’s Biggest Experiment?

    McKinsey now counts 25,000 AI agents among its workforce — roughly one for every 1.6 human employees. That ratio, disclosed by CEO Bob Sternfels at the Consumer Electronics Show and confirmed by the firm, makes the consultancy’s internal agentic build-out one of the most aggressive in professional services.

    The numbers have moved quickly. Eighteen months ago, McKinsey operated a few thousand agents. Today, through its AI arm QuantumBlack, AI-related work accounts for 40% of the firm’s output. The agents have saved an estimated 1.5 million hours on search and synthesis tasks. Non-client-facing headcount has fallen 25%, yet output from those teams has risen 10%.

    Sternfels’s stated ambition is to pair every one of McKinsey’s 40,000 employees with at least one AI agent within the next 18 months.

    Scale versus substance

    The scale is eye-catching. Whether it is meaningful depends on what you count as an agent and how you measure the return.

    McKinsey’s rivals are openly sceptical. EY’s global engineering chief has argued that “a handful of agents do the heavy lifting” and that value should be tracked through efficiency KPIs, not headcount. PwC’s chief AI officer has called agent count “probably the wrong measure”, advocating instead for quality and workflow optimisation. Their counterargument is clear: a smaller fleet of high-performing agents, rigorously measured, may deliver more than a vast deployment still being calibrated.

    The critique lands on familiar ground. As maddaisy examined earlier today, PwC’s 2026 Global CEO Survey found that 56% of chief executives still cannot point to revenue gains from their AI investments. The deployment-versus-outcomes gap is the central tension in enterprise AI right now, and McKinsey’s bet raises the question of whether the firm is racing ahead of the same problem — or solving it.

    From advisory to infrastructure

    The more consequential shift may be in McKinsey’s business model. Sternfels described a move away from the firm’s traditional fee-for-service approach toward a model where McKinsey works with clients to identify joint business cases and then helps underwrite the outcomes.

    This is a significant departure for a firm built on advisory fees and billable hours. It positions McKinsey less as a strategic counsellor and more as an infrastructure partner — one that brings its own AI workforce to bear on client problems and shares in the measurable results.

    QuantumBlack, with 1,700 people, now drives all of McKinsey’s AI initiatives. Alex Singla, the senior partner who co-leads the unit, has described the firm’s evolving recruitment profile: candidates who can move fluidly between traditional consulting and engineering, and who can work alongside AI rather than simply directing it.

    Boston Consulting Group is pursuing a similar direction, deploying “forward-deployed consultants” who build AI tools directly on client projects. But McKinsey’s scale of internal adoption — 25,000 agents embedded across the firm — gives it a data advantage that is harder to replicate. Every internal deployment generates operational insight into what works, what fails, and how agentic systems behave at enterprise scale.

    The governance question maddaisy has been tracking

    The timing of McKinsey’s announcement is worth noting against the backdrop of the agentic AI governance gap maddaisy covered earlier this week. Deloitte’s data showed that only 21% of companies have mature governance models for agentic AI, even as three-quarters plan to deploy it within two years. And a broader pattern has emerged across maddaisy’s recent coverage: enterprises are strategically confident about AI but operationally underprepared.

    McKinsey, as both a deployer and an adviser, sits at the intersection of this tension. If the firm can demonstrate that 25,000 agents operate reliably at scale — with governance, measurement, and accountability frameworks to match — it will have built the most persuasive case study in the industry. If the agents outrun oversight, the reputational exposure is equally significant. When an AI agent produces an analysis and the recommendation proves wrong, the liability question is not academic.

    What practitioners should watch

    For consulting professionals and enterprise leaders watching this play out, three things matter more than the headline number.

    First, the metric that matters is not agent count but outcome attribution. McKinsey’s 1.5 million hours saved is a process metric. The firm’s shift to underwriting client outcomes suggests it understands the need to move beyond efficiency and toward measurable business impact — the same gap that PwC’s CEO Survey identified industry-wide.

    Second, the talent model is changing faster than many firms acknowledge. McKinsey’s search for hybrid consultant-engineers, and BCG’s forward-deployed model, signal that the traditional consulting skill set is being augmented, not just supported, by AI fluency. Firms that treat AI as a productivity tool rather than a workforce design challenge will fall behind.

    Third, scale creates its own governance requirements. As McKinsey’s own Carolyn Dewar argued in Fortune, the real risk is not the technology but how leaders manage the fear and trust dynamics that surround it. Deploying 25,000 agents without the organisational infrastructure to govern them would validate every concern the firm’s rivals have raised.

    McKinsey’s wager is that first-mover scale in agentic AI creates a compounding advantage — more data, better workflows, stronger client proof points. The industry is about to find out whether volume leads to value, or whether a smaller, sharper approach gets there first.